
  ___  ____  ____  ____  ____ (R)
 /__    /   ____/   /   ____/
___/   /   /___/   /   /___/   15.1   Copyright 1985-2017 StataCorp LLC
  Statistics/Data Analysis            StataCorp
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32-user 64-core Stata network perpetual license:
       Serial number:  501506200495
         Licensed to:  Harvard-MIT Data Center
                       Cambridge, MA

Notes:
      1.  Stata is running in batch mode.
      2.  Unicode is supported; see help unicode_advice.
      3.  More than 2 billion observations are allowed; see help obs_advice.
      4.  Maximum number of variables is set to 5000; see help set_maxvar.

. do "dofiles/10_Table_A6.do" 

. /****************************************************************************
> *****************************
> Replication Files for Housing Discrimination and the Toxics Exposure Gap in t
> he United States: 
> Evidence from the Rental Market  by Peter Christensen, Ignacio Sarmiento-Barb
> ieri and Christopher Timmins
> *****************************************************************************
> ****************************/
. *Note: LPM stars need to be corrected by hand, estout prints starts of a test
>  against the null of zero
. * the test againste the null of one has to be manually coded
. 
. clear all

. set matsize 11000

. 
. use "../stores/toxic_discrimination_data.dta"

. 
. 
. keep if sample==1
(801 observations deleted)

. 
. loc quartiles 4

. 
. set seed 1010101

. 
. eststo clear

. 
. *****************************************************************************
> *******************
. * Toxic Concentration
. *****************************************************************************
> *******************
. *****************************************************************************
> *******************
. * Minority
. *****************************************************************************
> *******************
. *Note: same results if run the reghdfe for each bin
. 
. 
. eststo modelA11: disc_boot choice Minority_dec2 Minority_dec3 Minority_dec4
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -988.39053  
Iteration 1:   log pseudolikelihood =  -985.6732  
Iteration 2:   log pseudolikelihood = -985.67314  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(3)      =      59.26
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -985.67314               Pseudo R2         =     0.0141

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5395173   .1158791    -4.66   0.000    -.7301215   -.3489131
Minority_d~3 |  -.3520056   .1418611    -2.48   0.013    -.5853464   -.1186648
Minority_d~4 |   .1703064   .1349241     1.26   0.207     -.051624    .3922369
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5395173    .134234    -4.02   0.001    -.7772368   -.3017978
Minority_d~3 |  -.3520056   .1425153    -2.47   0.028    -.6043908   -.0996204
Minority_d~4 |   .1703064   .1443443     1.18   0.259    -.0853176    .4259305
------------------------------------------------------------------------------

. estimates store modelA11

. eststo modelA12: disc_boot choice Minority_dec2 Minority_dec3 Minority_dec4 ,
>  varlist(i.gender)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -981.97702  
Iteration 1:   log pseudolikelihood = -978.77984  
Iteration 2:   log pseudolikelihood = -978.77968  
Iteration 3:   log pseudolikelihood = -978.77968  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(4)      =     102.84
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -978.77968               Pseudo R2         =     0.0210

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5344545   .1167481    -4.58   0.000    -.7264881   -.3424209
Minority_d~3 |  -.3391341   .1354182    -2.50   0.012    -.5618772    -.116391
Minority_d~4 |   .1697548   .1265243     1.34   0.180    -.0383591    .3778687
             |
      gender |
       male  |  -.3029554   .0760311    -3.98   0.000    -.4280155   -.1778953
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5344545   .1358881    -3.93   0.002    -.7751033   -.2938056
Minority_d~3 |  -.3391341   .1358082    -2.50   0.027    -.5796413   -.0986268
Minority_d~4 |   .1697548   .1354686     1.25   0.232     -.070151    .4096606
------------------------------------------------------------------------------

. estimates store modelA12

. eststo modelA13: disc_boot choice Minority_dec2 Minority_dec3 Minority_dec4 ,
>  varlist(i.gender i.education_level)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -978.62948  
Iteration 1:   log pseudolikelihood = -975.03781  
Iteration 2:   log pseudolikelihood = -975.03747  
Iteration 3:   log pseudolikelihood = -975.03747  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(6)      =     101.06
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -975.03747               Pseudo R2         =     0.0247

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5440681   .1264796    -4.30   0.000    -.7521085   -.3360277
Minority_d~3 |  -.3404748   .1342906    -2.54   0.011    -.5613632   -.1195865
Minority_d~4 |   .1715921   .1220586     1.41   0.160    -.0291763    .3723606
             |
      gender |
       male  |  -.2999861   .0759767    -3.95   0.000    -.4249567   -.1750156
             |
education_~l |
        low  |  -.1412231   .1135603    -1.24   0.214    -.3280132     .045567
     medium  |  -.2569257   .1353053    -1.90   0.058    -.4794832   -.0343682
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5440681   .1416309    -3.84   0.002     -.794887   -.2932492
Minority_d~3 |  -.3404748   .1334106    -2.55   0.024    -.5767362   -.1042135
Minority_d~4 |   .1715921   .1319647     1.30   0.216    -.0621085    .4052928
------------------------------------------------------------------------------

. estimates store modelA13

. eststo modelA14: disc_boot choice Minority_dec2 Minority_dec3 Minority_dec4 ,
>  varlist(i.gender i.education_level i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -925.74055  
Iteration 1:   log pseudolikelihood = -916.48806  
Iteration 2:   log pseudolikelihood = -916.46513  
Iteration 3:   log pseudolikelihood = -916.46513  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(8)      =     120.56
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -916.46513               Pseudo R2         =     0.0833

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5211012   .1376313    -3.79   0.000    -.7474846   -.2947179
Minority_d~3 |  -.3482699   .1358951    -2.56   0.010    -.5717975   -.1247423
Minority_d~4 |   .1434486   .1312026     1.09   0.274    -.0723605    .3592577
             |
      gender |
       male  |  -.3129561   .0870571    -3.59   0.000    -.4561522     -.16976
             |
education_~l |
        low  |  -.1674876    .115987    -1.44   0.149    -.3582693     .023294
     medium  |  -.2999306   .1376423    -2.18   0.029    -.5263321   -.0735291
             |
       order |
          2  |  -.3356589   .2439099    -1.38   0.169     -.736855    .0655371
          3  |  -.9002829   .2019871    -4.46   0.000    -1.232522   -.5680438
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.5211012   .1554043    -3.35   0.005    -.7963119   -.2458906
Minority_d~3 |  -.3482699   .1367799    -2.55   0.024     -.590498   -.1060418
Minority_d~4 |   .1434486   .1396337     1.03   0.323    -.1038334    .3907307
------------------------------------------------------------------------------

. estimates store modelA14

. 
. reghdfe choice  Minority_dec2 Minority_dec3 Minority_dec4, absorb(gender educ
> ation_level order  Address ) cl(Zip_Code) level(90)  keepsing
WARNING: Singleton observations not dropped; statistical significance is biased
>  (link)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      6,723
Absorbing 4 HDFE groups                           F(   3,     13) =      16.03
Statistics robust to heteroskedasticity           Prob > F        =     0.0001
                                                  R-squared       =     0.6274
                                                  Adj R-squared   =     0.4401
                                                  Within R-sq.    =     0.0057
Number of clusters (Zip_Code) =         14        Root MSE        =     0.3614

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |  -.0623207   .0155598    -4.01   0.001     -.089876   -.0347654
Minority_d~3 |  -.0468917   .0167374    -2.80   0.015    -.0765326   -.0172508
Minority_d~4 |   .0223421   .0196357     1.14   0.276    -.0124315    .0571157
       _cons |   .3938288    .007636    51.58   0.000      .380306    .4073516
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------+
     Absorbed FE | Categories  - Redundant  = Num. Coefs |
-----------------+---------------------------------------|
          gender |         2           0           2     |
 education_level |         3           1           2     |
           order |         3           1           2    ?|
         Address |      2241        2241           0    *|
---------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

. 
. 
. 
. eststo modelA15: nlcom  ( Minority_dec2: ((_b[_cons]+_b[Minority_dec2])/(1-(_
> b[_cons]+_b[Minority_dec2])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Minority_dec3: ((_b[_cons]+_b[Minority_dec3])/(1-(_
> b[_cons]+_b[Minority_dec3])))/(_b[_cons]/(1-_b[_cons]))) ///           
>                         ( Minority_dec4: ((_b[_cons]+_b[Minority_dec4])/(1-(_
> b[_cons]+_b[Minority_dec4])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         , level(90) post

Minority_d~2:  ((_b[_cons]+_b[Minority_dec2])/(1-(_b[_cons]+_b[Minority_dec2]))
> )/(_b[_cons]/(1-_b[_cons]))
Minority_d~3:  ((_b[_cons]+_b[Minority_dec3])/(1-(_b[_cons]+_b[Minority_dec3]))
> )/(_b[_cons]/(1-_b[_cons]))
Minority_d~4:  ((_b[_cons]+_b[Minority_dec4])/(1-(_b[_cons]+_b[Minority_dec4]))
> )/(_b[_cons]/(1-_b[_cons]))

------------------------------------------------------------------------------
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_d~2 |   .7632834   .0531367    14.36   0.000     .6758813    .8506856
Minority_d~3 |   .8176804   .0592622    13.80   0.000     .7202029     .915158
Minority_d~4 |    1.09717    .089143    12.31   0.000     .9505424    1.243797
------------------------------------------------------------------------------

. 
. ****************************************************************************
. *These have to be added by hand
. forvalues i=2/4{
  2.        test _b[Minority_dec`i'] == 1
  3. }

 ( 1)  Minority_dec2 = 1

           chi2(  1) =   19.85
         Prob > chi2 =    0.0000

 ( 1)  Minority_dec3 = 1

           chi2(  1) =    9.46
         Prob > chi2 =    0.0021

 ( 1)  Minority_dec4 = 1

           chi2(  1) =    1.19
         Prob > chi2 =    0.2757

. ****************************************************************************
. 
. /* Also works, same result
> sum choice if dec2==1 & White==1 
> loc mean_2=r(mean)
> sum choice if dec3==1 & White==1 
> loc mean_3=r(mean)
> sum choice if dec4==1 & White==1 
> loc mean_4=r(mean)
> 
> nlcom  ( Minority_dec2: ((`mean_2'+_b[Minority_dec2])/(1-(`mean_2'+_b[Minorit
> y_dec2])))/(`mean_2'/(1-`mean_2'))) ///
>        ( Minority_dec3: ((`mean_3'+_b[Minority_dec3])/(1-(`mean_3'+_b[Minorit
> y_dec3])))/(`mean_3'/(1-`mean_3'))) ///
>        ( Minority_dec4: ((`mean_4'+_b[Minority_dec4])/(1-(`mean_4'+_b[Minorit
> y_dec4])))/(`mean_4'/(1-`mean_4'))) , level(90) post
> 
>                         
> *these have to be added by hand
> forvalues i=2/4{
>        test _b[Minority_dec`i'] == 1
> }
> 
> */
. *****************************************************************************
> *******************
. * African American vs Hispanic/LatinX
. *****************************************************************************
> *******************
. eststo: disc_boot choice Black_dec2 Black_dec3 Black_dec4 Hispanic_dec2 Hispa
> nic_dec3 Hispanic_dec4 
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -972.96819  
Iteration 1:   log pseudolikelihood = -968.44681  
Iteration 2:   log pseudolikelihood = -968.44596  
Iteration 3:   log pseudolikelihood = -968.44596  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(6)      =      64.77
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -968.44596               Pseudo R2         =     0.0313

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.7853473   .1340379    -5.86   0.000     -1.00582   -.5648746
  Black_dec3 |  -.6350542   .2167297    -2.93   0.003    -.9915428   -.2785656
  Black_dec4 |   .0261844   .1810569     0.14   0.885    -.2716277    .3239966
Hispanic_d~2 |  -.3012771   .1169441    -2.58   0.010     -.493633   -.1089212
Hispanic_d~3 |  -.0803406   .0819671    -0.98   0.327    -.2151645    .0544833
Hispanic_d~4 |   .3168186   .1489953     2.13   0.033     .0717432     .561894
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.7853473   .1693438    -4.64   0.000    -1.085244   -.4854507
  Black_dec3 |  -.6350542   .2139006    -2.97   0.011    -1.013858   -.2562505
  Black_dec4 |   .0261844    .182092     0.14   0.888    -.2962884    .3486572
Hispanic_d~2 |  -.3012771   .1234142    -2.44   0.030    -.5198355   -.0827188
Hispanic_d~3 |  -.0803406   .0826944    -0.97   0.349    -.2267869    .0661057
Hispanic_d~4 |   .3168186   .1663883     1.90   0.079     .0221561    .6114812
------------------------------------------------------------------------------
(est6 stored)

. estimates store modelA21

. eststo: disc_boot choice Black_dec2 Black_dec3 Black_dec4 Hispanic_dec2 Hispa
> nic_dec3 Hispanic_dec4, varlist(i.gender)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -966.97454  
Iteration 1:   log pseudolikelihood =  -961.9723  
Iteration 2:   log pseudolikelihood = -961.97156  
Iteration 3:   log pseudolikelihood = -961.97156  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(7)      =     107.34
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -961.97156               Pseudo R2         =     0.0378

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.7942401   .1333547    -5.96   0.000    -1.013589   -.5748912
  Black_dec3 |  -.6141067   .2099531    -2.92   0.003    -.9594488   -.2687645
  Black_dec4 |   .0269215   .1732308     0.16   0.876    -.2580178    .3118607
Hispanic_d~2 |  -.2856483      .1174    -2.43   0.015     -.478754   -.0925425
Hispanic_d~3 |  -.0777431   .0786975    -0.99   0.323     -.207189    .0517027
Hispanic_d~4 |   .3143595   .1432176     2.19   0.028     .0787876    .5499315
             |
      gender |
       male  |  -.2969566   .0810546    -3.66   0.000    -.4302795   -.1636336
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.7942401   .1699345    -4.67   0.000    -1.095183   -.4932974
  Black_dec3 |  -.6141067   .2059271    -2.98   0.011    -.9787899   -.2494234
  Black_dec4 |   .0269215   .1742815     0.15   0.880    -.2817194    .3355623
Hispanic_d~2 |  -.2856483   .1244199    -2.30   0.039    -.5059877   -.0653088
Hispanic_d~3 |  -.0777431   .0795681    -0.98   0.346     -.218653    .0631667
Hispanic_d~4 |   .3143595    .159566     1.97   0.071     .0317788    .5969402
------------------------------------------------------------------------------
(est7 stored)

. estimates store modelA22

. eststo: disc_boot choice Black_dec2 Black_dec3 Black_dec4 Hispanic_dec2 Hispa
> nic_dec3 Hispanic_dec4, varlist(i.gender i.education_level)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -963.12832  
Iteration 1:   log pseudolikelihood = -957.68145  
Iteration 2:   log pseudolikelihood =  -957.6805  
Iteration 3:   log pseudolikelihood =  -957.6805  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(9)      =     264.83
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -957.6805               Pseudo R2         =     0.0421

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.8166215   .1404865    -5.81   0.000    -1.047701   -.5855418
  Black_dec3 |  -.6188409   .2068056    -2.99   0.003    -.9590059    -.278676
  Black_dec4 |   .0227659   .1678721     0.14   0.892     -.253359    .2988909
Hispanic_d~2 |  -.2893643    .128596    -2.25   0.024    -.5008858   -.0778427
Hispanic_d~3 |   -.077899   .0816711    -0.95   0.340     -.212236     .056438
Hispanic_d~4 |   .3214732    .140336     2.29   0.022      .090641    .5523055
             |
      gender |
       male  |  -.2937429    .081757    -3.59   0.000    -.4282212   -.1592645
             |
education_~l |
        low  |  -.1756367   .1187612    -1.48   0.139    -.3709814     .019708
     medium  |  -.2768632   .1252477    -2.21   0.027    -.4828774    -.070849
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.8166215   .1738301    -4.70   0.000    -1.124463   -.5087799
  Black_dec3 |  -.6188409   .1996723    -3.10   0.008    -.9724473   -.2652346
  Black_dec4 |   .0227659   .1689755     0.13   0.895    -.2764785    .3220104
Hispanic_d~2 |  -.2893643   .1336385    -2.17   0.050    -.5260291   -.0526995
Hispanic_d~3 |   -.077899   .0824653    -0.94   0.362    -.2239396    .0681416
Hispanic_d~4 |   .3214732   .1587919     2.02   0.064     .0402633    .6026832
------------------------------------------------------------------------------
(est8 stored)

. estimates store modelA23

. eststo: disc_boot choice Black_dec2 Black_dec3 Black_dec4 Hispanic_dec2 Hispa
> nic_dec3 Hispanic_dec4, varlist(i.gender i.education_level i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -910.23067  
Iteration 1:   log pseudolikelihood = -899.77346  
Iteration 2:   log pseudolikelihood = -899.71428  
Iteration 3:   log pseudolikelihood = -899.71427  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(11)     =    1743.92
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -899.71427               Pseudo R2         =     0.1000

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.8083192   .1492354    -5.42   0.000    -1.053789   -.5628489
  Black_dec3 |  -.6198705   .2013075    -3.08   0.002    -.9509919   -.2887491
  Black_dec4 |  -.0088093   .1651946    -0.05   0.957    -.2805303    .2629117
Hispanic_d~2 |  -.2521266    .134985    -1.87   0.062    -.4741571    -.030096
Hispanic_d~3 |  -.0790205   .0917728    -0.86   0.389    -.2299734    .0719324
Hispanic_d~4 |   .2938816   .1543903     1.90   0.057     .0399321    .5478311
             |
      gender |
       male  |  -.3068188   .0902902    -3.40   0.001     -.455333   -.1583047
             |
education_~l |
        low  |  -.2085472   .1269706    -1.64   0.100    -.4173952    .0003009
     medium  |  -.3217568   .1289106    -2.50   0.013    -.5337959   -.1097176
             |
       order |
          2  |  -.3274132   .2524973    -1.30   0.195    -.7427343    .0879078
          3  |  -.9038518   .2104678    -4.29   0.000    -1.250041    -.557663
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.8083192   .1853143    -4.36   0.001    -1.136498   -.4801399
  Black_dec3 |  -.6198705   .1960878    -3.16   0.008     -.967129    -.272612
  Black_dec4 |  -.0088093   .1656711    -0.05   0.958    -.3022017    .2845831
Hispanic_d~2 |  -.2521266   .1399496    -1.80   0.095    -.4999679   -.0042852
Hispanic_d~3 |  -.0790205   .0933243    -0.85   0.412    -.2442917    .0862507
Hispanic_d~4 |   .2938816   .1755685     1.67   0.118    -.0170384    .6048017
------------------------------------------------------------------------------
(est9 stored)

. estimates store modelA24

. reghdfe choice Black_dec2 Black_dec3 Black_dec4 Hispanic_dec2 Hispanic_dec3 H
> ispanic_dec4, absorb(gender education_level order  Address ) cl(Zip_Code) lev
> el(90)  keepsing
WARNING: Singleton observations not dropped; statistical significance is biased
>  (link)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      6,723
Absorbing 4 HDFE groups                           F(   6,     13) =       8.77
Statistics robust to heteroskedasticity           Prob > F        =     0.0006
                                                  R-squared       =     0.6305
                                                  Adj R-squared   =     0.4443
                                                  Within R-sq.    =     0.0138
Number of clusters (Zip_Code) =         14        Root MSE        =     0.3601

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |  -.0959911    .018702    -5.13   0.000    -.1291111    -.062871
  Black_dec3 |  -.0828067   .0238844    -3.47   0.004    -.1251043    -.040509
  Black_dec4 |  -.0002636   .0243059    -0.01   0.992    -.0433078    .0427805
Hispanic_d~2 |  -.0286502   .0144172    -1.99   0.068    -.0541821   -.0031184
Hispanic_d~3 |  -.0110519   .0114821    -0.96   0.353    -.0313859    .0092821
Hispanic_d~4 |   .0450033   .0231702     1.94   0.074     .0039705    .0860361
       _cons |   .3938369   .0076483    51.49   0.000     .3802922    .4073816
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------+
     Absorbed FE | Categories  - Redundant  = Num. Coefs |
-----------------+---------------------------------------|
          gender |         2           0           2     |
 education_level |         3           1           2     |
           order |         3           1           2    ?|
         Address |      2241        2241           0    *|
---------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

. eststo modelA25: nlcom  ( Black_dec2: ((_b[_cons]+_b[Black_dec2])/(1-(_b[_con
> s]+_b[Black_dec2])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Black_dec3: ((_b[_cons]+_b[Black_dec3])/(1-(_b[_con
> s]+_b[Black_dec3])))/(_b[_cons]/(1-_b[_cons]))) ///           
>                         ( Black_dec4: ((_b[_cons]+_b[Black_dec4])/(1-(_b[_con
> s]+_b[Black_dec4])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Hispanic_dec2: ((_b[_cons]+_b[Hispanic_dec2])/(1-(_
> b[_cons]+_b[Hispanic_dec2])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Hispanic_dec3: ((_b[_cons]+_b[Hispanic_dec3])/(1-(_
> b[_cons]+_b[Hispanic_dec3])))/(_b[_cons]/(1-_b[_cons]))) ///           
>                         ( Hispanic_dec4: ((_b[_cons]+_b[Hispanic_dec4])/(1-(_
> b[_cons]+_b[Hispanic_dec4])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         , level(90) post

  Black_dec2:  ((_b[_cons]+_b[Black_dec2])/(1-(_b[_cons]+_b[Black_dec2])))/(_b[
> _cons]/(1-_b[_cons]))
  Black_dec3:  ((_b[_cons]+_b[Black_dec3])/(1-(_b[_cons]+_b[Black_dec3])))/(_b[
> _cons]/(1-_b[_cons]))
  Black_dec4:  ((_b[_cons]+_b[Black_dec4])/(1-(_b[_cons]+_b[Black_dec4])))/(_b[
> _cons]/(1-_b[_cons]))
Hispanic_d~2:  ((_b[_cons]+_b[Hispanic_dec2])/(1-(_b[_cons]+_b[Hispanic_dec2]))
> )/(_b[_cons]/(1-_b[_cons]))
Hispanic_d~3:  ((_b[_cons]+_b[Hispanic_dec3])/(1-(_b[_cons]+_b[Hispanic_dec3]))
> )/(_b[_cons]/(1-_b[_cons]))
Hispanic_d~4:  ((_b[_cons]+_b[Hispanic_dec4])/(1-(_b[_cons]+_b[Hispanic_dec4]))
> )/(_b[_cons]/(1-_b[_cons]))

------------------------------------------------------------------------------
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
  Black_dec2 |   .6528782   .0576161    11.33   0.000     .5581082    .7476482
  Black_dec3 |   .6948252   .0753771     9.22   0.000     .5708409    .8188095
  Black_dec4 |   .9988962   .1017193     9.82   0.000     .8315829     1.16621
Hispanic_d~2 |   .8854051   .0549008    16.13   0.000     .7951013    .9757088
Hispanic_d~3 |   .9545344   .0461735    20.67   0.000     .8785857    1.030483
Hispanic_d~4 |    1.20363   .1138283    10.57   0.000     1.016399    1.390861
------------------------------------------------------------------------------

. 
. 
. *these have to be added by hand
. foreach k in Black Hispanic{
  2.       forvalues i=2/4{
  3.              test _b[`k'_dec`i'] == 1
  4.       }
  5. }

 ( 1)  Black_dec2 = 1

           chi2(  1) =   36.30
         Prob > chi2 =    0.0000

 ( 1)  Black_dec3 = 1

           chi2(  1) =   16.39
         Prob > chi2 =    0.0001

 ( 1)  Black_dec4 = 1

           chi2(  1) =    0.00
         Prob > chi2 =    0.9913

 ( 1)  Hispanic_dec2 = 1

           chi2(  1) =    4.36
         Prob > chi2 =    0.0369

 ( 1)  Hispanic_dec3 = 1

           chi2(  1) =    0.97
         Prob > chi2 =    0.3248

 ( 1)  Hispanic_dec4 = 1

           chi2(  1) =    3.20
         Prob > chi2 =    0.0736

. *****************************************************************************
> *******************
. * Toxic Concentration
. *****************************************************************************
> *******************
. *****************************************************************************
> *******************
. * Distance
. *****************************************************************************
> *******************
. eststo: disc_boot choice Minority_within1  Minority_more1
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -991.37838  
Iteration 1:   log pseudolikelihood = -990.33394  
Iteration 2:   log pseudolikelihood =  -990.3339  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(2)      =      14.29
                                                Prob > chi2       =     0.0008
Log pseudolikelihood =  -990.3339               Pseudo R2         =     0.0094

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.1120485   .1369424    -0.82   0.413    -.3372987    .1132018
Minority_m~1 |  -.4192312   .1145535    -3.66   0.000     -.607655   -.2308074
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.1120485   .1396697    -0.80   0.437    -.3593941    .1352972
Minority_m~1 |  -.4192312   .1287544    -3.26   0.006    -.6472466   -.1912157
------------------------------------------------------------------------------
(est11 stored)

. estimates store modelB11

. eststo: disc_boot choice Minority_within1  Minority_more1 , varlist(i.gender)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -984.60768  
Iteration 1:   log pseudolikelihood = -983.00163  
Iteration 2:   log pseudolikelihood = -983.00161  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(3)      =      38.45
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -983.00161               Pseudo R2         =     0.0167

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.0991735    .130334    -0.76   0.447    -.3135539    .1152068
Minority_m~1 |  -.4184709   .1075584    -3.89   0.000    -.5953888    -.241553
             |
      gender |
       male  |  -.3120154   .0777247    -4.01   0.000    -.4398612   -.1841696
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.0991735   .1331775    -0.74   0.470    -.3350221     .136675
Minority_m~1 |  -.4184709   .1229199    -3.40   0.005     -.636154   -.2007879
------------------------------------------------------------------------------
(est12 stored)

. estimates store modelB12

. eststo: disc_boot choice Minority_within1  Minority_more1 , varlist(i.gender 
> i.education_level)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -981.28703  
Iteration 1:   log pseudolikelihood = -979.34241  
Iteration 2:   log pseudolikelihood = -979.34237  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(5)      =      76.03
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -979.34237               Pseudo R2         =     0.0204

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.0994896    .130396    -0.76   0.445    -.3139719    .1149927
Minority_m~1 |  -.4225248   .1110321    -3.81   0.000    -.6051564   -.2398932
             |
      gender |
       male  |  -.3087127   .0782271    -3.95   0.000    -.4373849   -.1800405
             |
education_~l |
        low  |  -.1549599   .1133425    -1.37   0.172    -.3413917     .031472
     medium  |  -.2526425   .1376818    -1.83   0.067    -.4791089   -.0261762
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.0994896   .1333384    -0.75   0.469     -.335623    .1366438
Minority_m~1 |  -.4225248   .1257491    -3.36   0.005    -.6452181   -.1998315
------------------------------------------------------------------------------
(est13 stored)

. estimates store modelB13

. eststo: disc_boot choice Minority_within1  Minority_more1 , varlist(i.gender 
> i.education_level i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -927.96374  
Iteration 1:   log pseudolikelihood = -920.17814  
Iteration 2:   log pseudolikelihood = -920.15913  
Iteration 3:   log pseudolikelihood = -920.15913  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(7)      =      84.74
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -920.15913               Pseudo R2         =     0.0796

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.1191289   .1396969    -0.85   0.394    -.3489098    .1106521
Minority_m~1 |  -.4127944   .1093093    -3.78   0.000    -.5925923   -.2329966
             |
      gender |
       male  |  -.3208997   .0892099    -3.60   0.000     -.467637   -.1741625
             |
education_~l |
        low  |  -.1822504   .1158593    -1.57   0.116    -.3728221    .0083212
     medium  |  -.2959499   .1392686    -2.13   0.034    -.5250263   -.0668735
             |
       order |
          2  |   -.343484   .2396691    -1.43   0.152    -.7377047    .0507366
          3  |  -.9051545    .201436    -4.49   0.000    -1.236487   -.5738217
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |  -.1191289   .1425262    -0.84   0.418    -.3715332    .1332755
Minority_m~1 |  -.4127944   .1280479    -3.22   0.007    -.6395587   -.1860301
------------------------------------------------------------------------------
(est14 stored)

. estimates store modelB14

. reghdfe choice Minority_within1  Minority_more1, absorb(gender education_leve
> l order  Address ) cl(Zip_Code) level(90)  keepsing
WARNING: Singleton observations not dropped; statistical significance is biased
>  (link)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      6,723
Absorbing 4 HDFE groups                           F(   2,     13) =       9.80
Statistics robust to heteroskedasticity           Prob > F        =     0.0025
                                                  R-squared       =     0.6268
                                                  Adj R-squared   =     0.4393
                                                  Within R-sq.    =     0.0040
Number of clusters (Zip_Code) =         14        Root MSE        =     0.3617

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |   -.014822   .0178278    -0.83   0.421    -.0463939    .0167498
Minority_m~1 |  -.0533972   .0121639    -4.39   0.001    -.0749386   -.0318558
       _cons |   .3938265   .0081092    48.57   0.000     .3794656    .4081873
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------+
     Absorbed FE | Categories  - Redundant  = Num. Coefs |
-----------------+---------------------------------------|
          gender |         2           0           2     |
 education_level |         3           1           2     |
           order |         3           1           2    ?|
         Address |      2241        2241           0    *|
---------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

. eststo modelB15: nlcom  ( Minority_within1: ((_b[_cons]+_b[Minority_within1])
> /(1-(_b[_cons]+_b[Minority_within1])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Minority_more1: ((_b[_cons]+_b[Minority_more1])/(1-
> (_b[_cons]+_b[Minority_more1])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         , level(90) post

Minority_w~1:  ((_b[_cons]+_b[Minority_within1])/(1-(_b[_cons]+_b[Minority_with
> in1])))/(_b[_cons]/(1-_b[_cons]))
Minority_m~1:  ((_b[_cons]+_b[Minority_more1])/(1-(_b[_cons]+_b[Minority_more1]
> )))/(_b[_cons]/(1-_b[_cons]))

------------------------------------------------------------------------------
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Minority_w~1 |   .9393942   .0707585    13.28   0.000     .8230067    1.055782
Minority_m~1 |   .7944336    .041791    19.01   0.000     .7256936    .8631736
------------------------------------------------------------------------------

. 
. *these have to be added by hand
. foreach i in within1 more1{
  2.        test _b[Minority_`i'] == 1
  3. }

 ( 1)  Minority_within1 = 1

           chi2(  1) =    0.73
         Prob > chi2 =    0.3917

 ( 1)  Minority_more1 = 1

           chi2(  1) =   24.20
         Prob > chi2 =    0.0000

. 
. *****************************************************************************
> *******************
. * African American vs Hispanic/LatinX
. *****************************************************************************
> *******************
.   
. 
. eststo: disc_boot choice Black_within1  Black_more1 Hispanic_within1  Hispani
> c_more1
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -976.74966  
Iteration 1:   log pseudolikelihood = -973.96022  
Iteration 2:   log pseudolikelihood = -973.95954  
Iteration 3:   log pseudolikelihood = -973.95954  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(4)      =      36.52
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -973.95954               Pseudo R2         =     0.0258

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.3568287   .2056602    -1.74   0.083    -.6951095   -.0185478
 Black_more1 |  -.6469435   .1715921    -3.77   0.000    -.9291874   -.3646995
Hispanic_w~1 |   .1341364   .0926937     1.45   0.148    -.0183311    .2866039
Hispanic_m~1 |  -.2003546   .0742553    -2.70   0.007    -.3224937   -.0782156
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.3568287   .2215958    -1.61   0.131    -.7492601    .0356028
 Black_more1 |  -.6469435   .1854996    -3.49   0.004     -.975451    -.318436
Hispanic_w~1 |   .1341364   .0964026     1.39   0.187    -.0365862     .304859
Hispanic_m~1 |  -.2003546   .0842468    -2.38   0.033    -.3495502   -.0511591
------------------------------------------------------------------------------
(est16 stored)

. sum choice if White==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      2,241    .3944668    .4888449          0          1

. estadd scalar responsewhite = r(mean), replace 

added scalar:
      e(responsewhite) =  .39446676

. estadd local gender = "", replace 

added macro:
             e(gender) : ""

. estadd local edu = "", replace 

added macro:
                e(edu) : ""

. estadd local order = "", replace 

added macro:
              e(order) : ""

. estimates store modelB21

. eststo: disc_boot choice Black_within1  Black_more1 Hispanic_within1  Hispani
> c_more1 , varlist(i.gender)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -970.37902  
Iteration 1:   log pseudolikelihood = -967.03412  
Iteration 2:   log pseudolikelihood = -967.03358  
Iteration 3:   log pseudolikelihood = -967.03358  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(5)      =      60.33
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -967.03358               Pseudo R2         =     0.0327

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |   -.336929   .2018555    -1.67   0.095    -.6689517   -.0049063
 Black_more1 |   -.651449   .1651154    -3.95   0.000    -.9230396   -.3798584
Hispanic_w~1 |   .1377426   .0884165     1.56   0.119    -.0076897    .2831749
Hispanic_m~1 |  -.1966931   .0660414    -2.98   0.003    -.3053216   -.0880646
             |
      gender |
       male  |  -.3062295   .0830137    -3.69   0.000    -.4427749    -.169684
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |   -.336929   .2182586    -1.54   0.147    -.7234505    .0495925
 Black_more1 |   -.651449   .1807533    -3.60   0.003     -.971551    -.331347
Hispanic_w~1 |   .1377426   .0921473     1.49   0.159    -.0254441    .3009293
Hispanic_m~1 |  -.1966931   .0766746    -2.57   0.024    -.3324788   -.0609075
------------------------------------------------------------------------------
(est17 stored)

. sum choice if White==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      2,241    .3944668    .4888449          0          1

. estadd scalar responsewhite = r(mean), replace 

added scalar:
      e(responsewhite) =  .39446676

. estadd local gender = "Yes", replace 

added macro:
             e(gender) : "Yes"

. estadd local edu = "", replace 

added macro:
                e(edu) : ""

. estadd local order = "", replace 

added macro:
              e(order) : ""

. estimates store modelB22

. eststo: disc_boot choice Black_within1  Black_more1 Hispanic_within1  Hispani
> c_more1 , varlist(i.gender i.education_level)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -966.55753  
Iteration 1:   log pseudolikelihood = -962.84006  
Iteration 2:   log pseudolikelihood = -962.83933  
Iteration 3:   log pseudolikelihood = -962.83933  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(7)      =     149.45
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -962.83933               Pseudo R2         =     0.0369

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.3426738   .2014384    -1.70   0.089    -.6740106    -.011337
 Black_more1 |  -.6617827   .1639522    -4.04   0.000    -.9314601   -.3921054
Hispanic_w~1 |   .1411009    .091637     1.54   0.124    -.0096286    .2918304
Hispanic_m~1 |  -.1974266   .0738891    -2.67   0.008    -.3189632   -.0758899
             |
      gender |
       male  |   -.302347   .0840452    -3.60   0.000     -.440589    -.164105
             |
education_~l |
        low  |  -.1888943   .1172357    -1.61   0.107    -.3817299    .0039413
     medium  |  -.2709857     .12907    -2.10   0.036     -.483287   -.0586844
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.3426738   .2184938    -1.57   0.141    -.7296117    .0442641
 Black_more1 |  -.6617827   .1794354    -3.69   0.003    -.9795509   -.3440146
Hispanic_w~1 |   .1411009    .095496     1.48   0.163    -.0280161     .310218
Hispanic_m~1 |  -.1974266   .0835504    -2.36   0.034    -.3453888   -.0494643
------------------------------------------------------------------------------
(est18 stored)

. sum choice if White==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      2,241    .3944668    .4888449          0          1

. estadd scalar responsewhite = r(mean), replace 

added scalar:
      e(responsewhite) =  .39446676

. estadd local gender = "Yes", replace 

added macro:
             e(gender) : "Yes"

. estadd local edu = "Yes", replace 

added macro:
                e(edu) : "Yes"

. estadd local order = "", replace 

added macro:
              e(order) : ""

. estimates store modelB23

. eststo: disc_boot choice Black_within1  Black_more1 Hispanic_within1  Hispani
> c_more1 , varlist(i.gender i.education_level i.order)
note: multiple positive outcomes within groups encountered.
note: 1,331 groups (3,993 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -912.97182  
Iteration 1:   log pseudolikelihood = -904.06377  
Iteration 2:   log pseudolikelihood = -904.01323  
Iteration 3:   log pseudolikelihood = -904.01323  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,730
                                                Wald chi2(9)      =     132.79
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -904.01323               Pseudo R2         =     0.0957

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.3696098   .2097833    -1.76   0.078    -.7146727   -.0245469
 Black_more1 |    -.65107   .1526792    -4.26   0.000     -.902205   -.3999351
Hispanic_w~1 |   .1408006   .0972992     1.45   0.148    -.0192423    .3008435
Hispanic_m~1 |  -.1898481   .0784416    -2.42   0.016    -.3188731   -.0608231
             |
      gender |
       male  |  -.3127156   .0925256    -3.38   0.001    -.4649066   -.1605246
             |
education_~l |
        low  |  -.2227868   .1254627    -1.78   0.076    -.4291545    -.016419
     medium  |  -.3160507   .1325095    -2.39   0.017    -.5340094   -.0980919
             |
       order |
          2  |  -.3392887   .2482428    -1.37   0.172    -.7476118    .0690343
          3  |  -.9105731    .210472    -4.33   0.000    -1.256769   -.5643776
------------------------------------------------------------------------------
cluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codecluster(Zip_Code)Zip_Codeclus
> ter(Zip_Code)Zip_Code

Bootstrap Corrected Estimates
------------------------------------------------------------------------------
             |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.3696098   .2265961    -1.63   0.127    -.7708965    .0316768
 Black_more1 |    -.65107   .1787903    -3.64   0.003    -.9676957   -.3344444
Hispanic_w~1 |   .1408006   .1032163     1.36   0.196    -.0419886    .3235898
Hispanic_m~1 |  -.1898481   .0873971    -2.17   0.049    -.3446225   -.0350737
------------------------------------------------------------------------------
(est19 stored)

. sum choice if White==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      2,241    .3944668    .4888449          0          1

. estadd scalar responsewhite = r(mean), replace 

added scalar:
      e(responsewhite) =  .39446676

. estadd local gender = "Yes", replace 

added macro:
             e(gender) : "Yes"

. estadd local edu = "Yes", replace 

added macro:
                e(edu) : "Yes"

. estadd local order = "Yes", replace 

added macro:
              e(order) : "Yes"

. estimates store modelB24

. loc d_resp `e(diff_response)'

. di `d_resp' 
.40606872

. reghdfe choice Black_within1  Black_more1 Hispanic_within1  Hispanic_more1, a
> bsorb(gender education_level order  Address ) cl(Zip_Code) level(90)  keepsin
> g
WARNING: Singleton observations not dropped; statistical significance is biased
>  (link)
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =      6,723
Absorbing 4 HDFE groups                           F(   4,     13) =      11.75
Statistics robust to heteroskedasticity           Prob > F        =     0.0003
                                                  R-squared       =     0.6298
                                                  Adj R-squared   =     0.4435
                                                  Within R-sq.    =     0.0119
Number of clusters (Zip_Code) =         14        Root MSE        =     0.3603

                              (Std. Err. adjusted for 14 clusters in Zip_Code)
------------------------------------------------------------------------------
             |               Robust
      choice |      Coef.   Std. Err.      t    P>|t|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |  -.0482652   .0271285    -1.78   0.099     -.096308   -.0002225
 Black_more1 |  -.0844434   .0171758    -4.92   0.000    -.1148606   -.0540263
Hispanic_w~1 |   .0187481   .0130972     1.43   0.176    -.0044463    .0419424
Hispanic_m~1 |  -.0225254   .0089136    -2.53   0.025    -.0383108     -.00674
       _cons |    .393836   .0081238    48.48   0.000     .3794493    .4082227
------------------------------------------------------------------------------

Absorbed degrees of freedom:
---------------------------------------------------------+
     Absorbed FE | Categories  - Redundant  = Num. Coefs |
-----------------+---------------------------------------|
          gender |         2           0           2     |
 education_level |         3           1           2     |
           order |         3           1           2    ?|
         Address |      2241        2241           0    *|
---------------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

. eststo modelB25: nlcom  ( Black_within1: ((_b[_cons]+_b[Black_within1])/(1-(_
> b[_cons]+_b[Black_within1])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Black_more1: ((_b[_cons]+_b[Black_more1])/(1-(_b[_c
> ons]+_b[Black_more1])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Hispanic_within1: ((_b[_cons]+_b[Hispanic_within1])
> /(1-(_b[_cons]+_b[Hispanic_within1])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         ( Hispanic_more1: ((_b[_cons]+_b[Hispanic_more1])/(1-
> (_b[_cons]+_b[Hispanic_more1])))/(_b[_cons]/(1-_b[_cons]))) ///
>                         , level(90) post

Black_with~1:  ((_b[_cons]+_b[Black_within1])/(1-(_b[_cons]+_b[Black_within1]))
> )/(_b[_cons]/(1-_b[_cons]))
 Black_more1:  ((_b[_cons]+_b[Black_more1])/(1-(_b[_cons]+_b[Black_more1])))/(_
> b[_cons]/(1-_b[_cons]))
Hispanic_w~1:  ((_b[_cons]+_b[Hispanic_within1])/(1-(_b[_cons]+_b[Hispanic_with
> in1])))/(_b[_cons]/(1-_b[_cons]))
Hispanic_m~1:  ((_b[_cons]+_b[Hispanic_more1])/(1-(_b[_cons]+_b[Hispanic_more1]
> )))/(_b[_cons]/(1-_b[_cons]))

------------------------------------------------------------------------------
      choice |      Coef.   Std. Err.      z    P>|z|     [90% Conf. Interval]
-------------+----------------------------------------------------------------
Black_with~1 |   .8127351   .0963129     8.44   0.000     .6543146    .9711557
 Black_more1 |   .6895304   .0533305    12.93   0.000     .6018095    .7772513
Hispanic_w~1 |   1.081039   .0588525    18.37   0.000     .9842356    1.177843
Hispanic_m~1 |   .9090253   .0343424    26.47   0.000     .8525371    .9655135
------------------------------------------------------------------------------

. 
. eststo modelB25: estadd scalar listings =  `e(N)'/3

added scalar:
           e(listings) =  2241

. eststo modelB25: estadd scalar diff_response= `d_resp'

added scalar:
      e(diff_response) =  .40606872

. sum choice if White==1 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
      choice |      2,241    .3944668    .4888449          0          1

. eststo modelB25: estadd scalar responsewhite = r(mean), replace 

added scalar:
      e(responsewhite) =  .39446676

. eststo modelB25: estadd local gender = "Yes", replace 

added macro:
             e(gender) : "Yes"

. eststo modelB25: estadd local edu = "Yes", replace 

added macro:
                e(edu) : "Yes"

. eststo modelB25: estadd local order = "Yes", replace 

added macro:
              e(order) : "Yes"

. *estimates store modelB25
. 
. foreach k in Black Hispanic{
  2.       foreach i in within1 more1{
  3.              test _b[`k'_`i'] == 1
  4.       }
  5. }

 ( 1)  Black_within1 = 1

           chi2(  1) =    3.78
         Prob > chi2 =    0.0519

 ( 1)  Black_more1 = 1

           chi2(  1) =   33.89
         Prob > chi2 =    0.0000

 ( 1)  Hispanic_within1 = 1

           chi2(  1) =    1.90
         Prob > chi2 =    0.1685

 ( 1)  Hispanic_more1 = 1

           chi2(  1) =    7.02
         Prob > chi2 =    0.0081

. 
. 
. 
. 
. 
. *****************************************************************************
> *******************
. * Export to latex
. * based on http://www.eyalfrank.com/code-riffs-stata-and-regression-tables/
. *****************************************************************************
> *******************
. 
. 
. 
. 
. ************************************************************
. * estout Panel A1
. ************************************************************
. 
. #delimit ; 
delimiter now ;
. esttab modelA11 
>        modelA12 
>        modelA13
>        modelA14
>        modelA15
>        using "../views/tableA6.tex", 
>        style(tex) 
>        eform(1 1 1 1 0)
>        cells(b(star fmt(4)) ci(par fmt(4) par(( , )))  )  
>        label 
>        noobs
>        mlabels(,none)  
>        nonumbers 
>        collabels(,none) 
>        eqlabels(,none)
>        varlabels(Minority_dec2 "Minority 0-25th perc. Tox. Conc." 
>                                  Minority_dec3 "Minority 25-75th perc. Tox. C
> onc."  
>                                  Minority_dec4 "Minority 75-100th perc. Tox. 
> Conc." ) 
>        starl(* 0.1 ** 0.05 *** 0.01)   
>        level(90)     
>        prehead(         
> \begin{table}[H]
> \tiny \centering
> \begin{threeparttable}
> \captionsetup{justification=centering}
>   \caption{Estimates of Discriminatory Constraint on Housing Choice: \\ Robus
> tness to Controls and Estimation Strategy}
>         \label{tab:02mainresults}
> \begin{tabular}{@{\extracolsep{5pt}}lccccc}
> \\[-1.8ex]\hline
> \hline \\[-1.8ex]
>  & \multicolumn{5}{c}{\textit{Dependent variable: {\it Response}}} \\
>  \cline{2-6} \\
> & \multicolumn{4}{c}{\textit{Conditional }} & {\it Linear} \\  
> & \multicolumn{4}{c}{\textit{ Logit}} & {\it Probability}\\
> & \multicolumn{4}{c}{\textit{ }}  & {\it Model}\\
> \cline{2-5} \\ \\[-1.8ex] 
> & (1) & (2) & (3) & (4)  & (5) \\ 
> \\[-1.8ex] 
> \hline \\[-1.8ex]
>  {\it Panel A: Quartiles of RSEI Tox. Conc.}\\
>  \hline \\[-1.8ex]
>        )
>        posthead({\it Panel A.1.: Minority } \\
>                                 &  &  &    \\) 
>       prefoot() 
>        postfoot(
>       \hline \\[-1.8ex] )
>        
>        replace;
(output written to ../views/tableA6.tex)

. #delimit cr
delimiter now cr
. 
. ************************************************************
. * estout Panel A2
. ************************************************************
. 
. #delimit ; 
delimiter now ;
. esttab modelA21 
>        modelA22 
>        modelA23
>        modelA24
>        modelA25
>        using "../views/tableA6.tex", 
>        style(tex)
>        eform(1 1 1 1 0) 
>        level(90)
>        cells(b(star fmt(4)) ci(par fmt(4) par(( , )))  )  
>        label 
>        noobs
>        mlabels(,none)
>        nonumbers 
>        collabels(,none)      
>        eqlabels(,none)
>        varlabels(Black_dec2 "Af. American 0-25th perc. Tox. Conc." 
>                                  Black_dec3 "Af. American 25-75th perc. Tox. 
> Conc." 
>                                  Black_dec4 "Af. American 75-100th perc. Tox.
>  Conc."
>                                  Hispanic_dec2 "Hispanic/LatinX 0-25th perc. 
> Tox. Conc." 
>                                  Hispanic_dec3 "Hispanic/LatinX 25-75th perc.
>  Tox. Conc." 
>                                  Hispanic_dec4 "Hispanic/LatinX 75-100th perc
> . Tox. Conc." ) 
>        starl(* 0.1 ** 0.05 *** 0.01)   
>        prehead( 
>        )
>        posthead({\it Panel A.2.: By Race }\\
>                 &  &  &    \\) 
>        prefoot() 
>        postfoot(
>             \\[-1.8ex]\hline 
>       \hline \\[-1.8ex] )
>        
>        append;
(output written to ../views/tableA6.tex)

. #delimit cr
delimiter now cr
. 
. 
. ************************************************************
. * estout Panel B1
. ************************************************************
. 
. #delimit ; 
delimiter now ;
. esttab modelB11 
>        modelB12 
>        modelB13
>        modelB14
>        modelB15
>        using "../views/tableA6.tex", 
>        style(tex)
>        eform(1 1 1 1 0) 
>        level(90)
>        cells(b(star fmt(4)) ci(par fmt(4) par(( , )))  )  
>        label 
>        noobs
>        mlabels(,none)
>        nonumbers 
>        collabels(,none)      
>        eqlabels(,none)
>        varlabels(Minority_within1 "TRI less than 1 mile $\times$ Minority"
>                                  Minority_more1 "TRI more than 1 mile $\times
> $  Minority") 
>        starl(* 0.1 ** 0.05 *** 0.01)   
>        prehead(  {\it Panel B: Proximity to TRI Plant}\\
>                                  \hline \\[-1.8ex]
>        )
>        posthead({\it Panel B.1.: Minority} \\
>                                  &  &  &    \\) 
>        prefoot() 
>        postfoot(
>       \hline \\[-1.8ex] )
>        
>        append;
(output written to ../views/tableA6.tex)

. #delimit cr
delimiter now cr
. 
. ************************************************************
. * estout Panel B2
. ************************************************************
. 
. #delimit ; 
delimiter now ;
. esttab modelB21 
>        modelB22 
>        modelB23
>        modelB24
>        modelB25
>        using "../views/tableA6.tex", 
>        style(tex) 
>        eform(1 1 1 1 0)
>        cells(b(star fmt(4)) ci(par fmt(4) par(( , )))  )  
>        label 
>        noobs
>        mlabels(,none)  
>        nonumbers
>        collabels(,none) 
>        eqlabels(,none)
>        varlabels(Hispanic_within1 "TRI less than 1 mile $\times$ Hispanic/Lat
> inX"
>                                  Hispanic_more1 "TRI more than 1 mile $\times
> $  Hispanic/LatinX"
>                                  Black_within1 "TRI less than 1 mile $\times$
>  African American"
>                                  Black_more1 "TRI more than 1 mile $\times$  
> African American") 
>        starl(* 0.1 ** 0.05 *** 0.01) 
>        stats(responsewhite
>              gender 
>              edu 
>              order
>              N
>              listings
>              diff_response, fmt(2 0 0 0 %9.0gc %9.0gc 2)
>              labels(" Mean Response (White)"
>                    "\hline Gender" 
>                    "Education Level" 
>                    "Inquiry Order"
>                    "\hline Observations"
>                    "Listings"
>                    "\% w. diff. response"
>                    )) 
>        level(90)     
>          prehead( 
>        )
>        posthead({\it Panel B.2.: By Race }\\
>                                          &  &  &    \\) 
>       prefoot() 
>        postfoot(           
> \hline
> \hline \\[-1.8ex]
> \end{tabular}
> \begin{tablenotes}[scriptsize,flushleft] \scriptsize
> \item Notes: 
> \end{tablenotes} 
> \end{threeparttable}
> \end{table})
>        append;
(output written to ../views/tableA6.tex)

. #delimit cr
delimiter now cr
. 
. 
. 
. *end 
. 
end of do-file
